Gning, A., Mihaylova, L., Maskell, S. et al. (2 more authors) (2011) Group Object Structure and State Estimation With Evolving Networks and Monte Carlo Methods. IEEE Transactions on Signal Processing, 59 (4). 1383 - 1396. ISSN 1053-587X
Abstract
This paper proposes a technique for motion estimation of groups of targets based on evolving graph networks. The main novelty over alternative group tracking techniques stems from learning the network structure for the groups. Each node of the graph corresponds to a target within the group. The uncertainty of the group structure is estimated jointly with the group target states. New group structure evolving models are proposed for automatic graph structure initialization, incorporation of new nodes, unexisting nodes removal, and the edge update. Both the state and the graph structure are updated based on range and bearing measurements. This evolving graph model is propagated combined with a sequential Monte Carlo framework able to cope with measurement origin uncertainty. The effectiveness of the proposed approach is illustrated over scenarios for group motion estimation in urban environments. Results with challenging scenarios with merging, splitting, and crossing of groups are presented with high estimation accuracy. The performance of the algorithm is also evaluated and shown on real ground moving target indicator (GMTI) radar data and in the presence of data origin uncertainty.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Evolving graphs; group target tracking; Metropolis-Hastings step; Monte Carlo methods; nonlinear estimation; random graphs |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Dec 2014 16:46 |
Last Modified: | 08 Nov 2016 18:07 |
Published Version: | http://dx.doi.org/10.1109/TSP.2010.2103062 |
Status: | Published |
Publisher: | IEEE |
Refereed: | No |
Identification Number: | 10.1109/TSP.2010.2103062 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:82275 |